Modern apparatuses such as aerospace or ground vehicles, propulsion systems, electrical power systems, industrial equipment and machines, etc are outfitted with sensors and digital processors for control and monitoring of the apparatuses. Diagnostics functions are control and monitoring functions that have to do with an abnormal, faulty, operation of the apparatus. The diagnostics functions could be used for improving safety of the system operation, e.g., by halting the operation or by switching to a degraded mode of operation; for improving system performance, e.g., by adapting the system control or scheduling; and for facilitating maintenance and repair, e.g., by providing a guidance on which of a plurality of possible maintenance actions should be undertaken.
The discussion below is related to computational method for diagnostic estimation. “Diagnostic estimation” here is defined as a diagnostic function determining which of plurality of fault conditions exist in the apparatus, e.g., which part of the apparatus is faulty, and further determining fault states. The “fault states” here are defined as quantitative characteristics of the fault conditions, e.g., an extent or degree of damage of the part. Parametric fault states are the fault states described by real numbers and discrete fault states are the fault states described by binaries, e.g., 0/1 or true/false, or by integer numbers. Diagnostic estimation computes statistical estimates of the fault states of the apparatus from the available apparatus data.
Most of embedded digital electronics used in modern systems includes low-level diagnostics functions. Such prior art diagnostic functions are usually univariate, i.e., each function is performed by observing a single signal. The univariate approach is usually adequate for detecting and diagnosing faults localized at a single embedded sensor or single control loop in the system. However, there is also an important need for detecting and diagnosing faults, which are not localized and that simultaneously impact readings of many sensors and/or outputs of many low-level diagnostic functions. Some of the prior art diagnostic systems integrate and process simultaneous fault diagnostics codes by using AI (artificial intelligence) reasoning methods. The AI methods are based on suboptimal heuristics because they are dealing with hard problems that have combinatorial complexity.
Some of the background technology includes multivariable model-based diagnostic estimation, which integrates and fuses the data from multiple sensors and multiple low-level diagnostics codes. Multivariable model-based methods are known in advanced control systems area. Though the multivariable advanced control methods pursue a different problem (control rather than the diagnostic estimation) they are related to the proposed method in the computational approach part. Much of prior multivariable control work is based on linear models and linear analysis methods. The linear models and methods cannot adequately address the multivariable diagnostic estimation problems because they cannot deal with nonlinearities, constraints, and system structure changes. A more relevant prior art is Model Predictive Control (MPC), which is a control method overwhelmingly used in process industries. MPC computes control at each time step by solving a batch optimization problem over a moving prediction horizon. The important advantages of MPC are that (i) it can handle constraints on the control or system variables and (ii) it can handle system structure changes such as missing sensor data and off-control actuators.
Conventional technology related to the subject of this invention includes Moving Horizon Estimation (MHE) algorithms. MHE is based on ideas related to the MPC but is aimed at estimation rather than at control problems. MHE computes estimates of hidden state parameters by solving a batch optimization problem over a moving horizon of past observation data; MHE optimizes model fit to the observation data. The MHE or a related optimization based approach can be used for multivariable diagnostics estimation, but there are two difficulties that need to be overcome. One difficulty is that the fault estimation problems are nonlinear. Another difficulty is that these problems include discrete variables that describe the presence or absence of faults. Incorrect estimation of these discrete variables could lead to false positives and false negatives in the fault detection; both types of errors are undesirable. Presence of the discrete variables could lead to combinatorial complexity of the problem.
MHE or other optimization-based methods can be implemented for diagnostic estimation by computing an optimal estimate of the fault parameters using standard algorithms for solving mixed problems with parametric and discrete estimated variables. Such algorithms used in the prior art include GA (Genetic Algorithms), MIQP (Mixed-integer Quadratic Programming), and MILP (Mixed-integer Linear Programming). Besides being inherently suboptimal in dealing with the combinatorial complexity, these methods are slow and, thus, not suitable for real time use in an embedded system or for centralized data processing for a large fleet of monitored devices (e.g., monitoring a fleet of engines). For example, U.S. Pat. No. 6,606,580 indicates that using GA optimization method for diagnostic estimation of faults in turbine engine required about an hour of computations.